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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Salau, Ayodeji Olalekan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Prediction of the risk of developing heart disease using logistic regression Salau, Ayodeji Olalekan; Assegie, Tsehay Admassu; Markus, Elisha Didam; Eneh, Joy Nnenna; Ozue, ThankGod Izuchukwu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1809-1815

Abstract

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD.
Comparative study of passive magnetic bearing using four ring magnets: a critical review Chaojun, Yang; Wondimu, Amberbir; Salau, Ayodeji Olalekan
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i2.pp685-695

Abstract

Researchers on reducing energy losses caused by friction in small-scale wind turbines. This is crucial because a significant amount of energy is wasted due to friction in the main roller bearing. In order to overcome this, the concept of levitation has gained popularity. Levitation is achieved by employing the repelling forces between two opposing poles of a permanent magnet (PM), significantly reducing friction between the turbine stator and rotor. As a result, the overall energy production of the turbine increases. The use of passive permanent magnet bearings has several disadvantages, such as limited load-bearing capacity and rigidity. To address these limitations, we conducted numerical studies on four different configurations in order to enhance load-bearing capacity and stiffness. The results showed that the radial configuration outperformed the axial-type configuration in terms of stiffness and load-bearing capabilities in all four arrangements. Furthermore, it was revealed that radial passive magnetic bearings with adequate air gaps are not only more efficient but also less expensive than employing iron cores at the rear and between ring magnets for small-scale wind turbines.
Security-based low-density parity check encoder for 5G communication Rajangam, Balamurugan; Alagarsamy, Manjunathan; Radhakrishnan, Chirakkal Rathish; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Quansah, Andrew; Chowdhury, Nur Mohammad; Chowdhury, Ismatul Jannat
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7019

Abstract

The fifth generation (5G) of mobile telecommunication standards is intended to offer better performance and efficiency. One of the most significant difficulties in delivering safe data transfer through the transmission channel in the emerging 5G technology is channel-coding security. This research primarily focused on offering information transmission that is secure in the place of novel assaults such as side-channel attacks. In this research, we present a low-density parity check (LDPC) encoder that is constructed using the multiplicative masking method to overcome side-channel attacks, one of the most significant security concerns for the upcoming 5G system. As a result, it offers greater security, reduced complexity, and higher performance. Power, area, and delay can all be calculated using LDPC codes. Comparing multiplicative masking implemented LDPC encoders to ordinary channel coding techniques in terms of security seen that multiplicative masking implemented LDPC encoders offer more security. The program Xilinx ISE 14.7 can synthesize the analysis. The advantage of multiplicative masking is that it offers a promising level of security through the principle of randomization, which is the foundation of the procedure. According to the analysis, the secured transmission of the data by the proposed multiplicative masking implemented LDPC encoder is increased.
Amharic event text classification from social media using hybrid deep learning Ayalew, Amogne Andualem; Tegegne, Melaku Lake; Manivannan, Bommy; Suresh, Tamilarasi; Kumar, Napa Komal; Prasad, Battula Krishna; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2264-2270

Abstract

This study aims to develop a hybrid deep-learning model for detecting and classifying Amharic text. Various natural language applications, such as information extraction, event extraction, conversation, text summarization, and require an automatic event classification. However, existing studies focused on classification, giving little attention to the preprocessing and feature extraction techniques. To address this problem, this work proposed a hybridized deep learning-based Amharic social media text event classification model. The model consists of word-to-vector (Word2vecv) word embedding techniques to capture the semantic and syntactic representation. Convolutional neural network (CNN) is used to extract short-length text features. Additionally, bidirectional long-short memory (Bi-LSTM) is used to extract features from long Amharic sentences and classify those events based on their classes. The dataset used for training and testing consists of 6,740 labeled Amharic text sentences, collected from social media. The result shows an accuracy of 94.8% in detecting and classifying Amharic text events.
Soybean leaf disease detection and classification using deep learning approach Adimas, Ayenew Kassie; Mekonen, Mareye Zeleke; Assegie, Tsehay Admassu; Singh, Hemant Kumar; Mazumdar, Indu; Gupta, Shashi Kant; Salau, Ayodeji Olalekan; Tin, Ting Tin
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8585

Abstract

In Ethiopia, where soybeans are mainly involved, manual observation has traditionally been relied upon for detecting soybean leaf diseases. However, the manual process is susceptible to numerous issues such as labor-intensiveness, inconsistency, and subjectivity. While previous studies have explored automated classification for soybean leaf disease detection, they primarily focused on binary classification, overlooking the complexity and diversity of soybean leaf diseases, which hinders effective management strategies. This study introduces deep learning algorithms and computer vision for automated soybean leaf disease identification and classification in soybean leaves. By comparing pre-trained convolutional neural network (CNN) models (VGG16, VGG19, and ResNet50V2), a dataset of 3078 soybean leaf images was curated, representing various diseases. Image preprocessing techniques augmented the dataset to 6,958 images, enhancing the model's accuracy and generalization performance. VGG16 demonstrated outstanding performance with a test accuracy of 99.35%, highlighting its promising performance and generalization potential.
Co-Authors Adimas, Ayenew Kassie Alagarsamy, Manjunathan Assegie, Tsehay Admassu Ayalew, Amogne Andualem Chaojun, Yang Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Eneh, Joy Nnenna Gupta, Shashi Kant Kumar, Napa Komal Manivannan, Bommy Markus, Elisha Didam Mazumdar, Indu Mekonen, Mareye Zeleke Ozue, ThankGod Izuchukwu Prasad, Battula Krishna Quansah, Andrew Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Singh, Hemant Kumar Suresh, Tamilarasi Tegegne, Melaku Lake Tin, Ting Tin Wondimu, Amberbir